import numpy as np
from sklearn_linear_imodel import SGDRegression
x=np.array([[100],[113],[90],[89],[60],[70],[50],[45],[55],[78]])
y=np.array([[301],[324],[285],[296],[200],[260],[300],[120],[180],[245]])
model=SGDRegression(loss='huber',max_iter=5000,random_state=42)
model.fit(x,y.ravel())
print("w=",model.coef_[0],",b=",model.intercept_)